Jesada Kajornrit

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This paper proposes a methodology to create an interpretable fuzzy model for monthly rainfall time series prediction. The proposed methodology incorporates the advantages of artificial neural network, fuzzy logic and genetic algorithm. In the first step, the differences between the time series data are calculated and they are used to define the interval(More)
Recommendation systems, also known as intelligent decision support systems, have been used to support and strengthen the decision making in various areas including education. In order to establish efficient recommendation systems for educational purposes, several specific problems have to be addressed. One such problem is the weak relationship between input(More)
Ground-based rainfall observations are the primary sources of precipitation data used in most developing countries. However, those observations are frequently damaged or incomplete, thus missing data is always a problem. This comparison study examines a number of spatial interpolation methods used to estimate missing monthly rainfall data in the northeast(More)
Accurate rainfall time series prediction is one of the important tasks in hydrological study. A conventional time series model such as autoregressive moving average or an intelligent model such as artificial neural network have been used efficiently to perform this task. However, such models are difficult to interpret by human analysts because their(More)
This paper proposes a comparative study of commonly-used global optimization methods to improve training performance of back-propagation neural networks. The optimization methods adopted herein include Simulated annealing, Direct search, and Genetic algorithm. These methods are used to optimize neural networks' weights and biases before using(More)